Чернов Григорий Витальевич Модели обучения в экономических экспериментах

Краткое описание

Чернов Григорий Витальевич Модели обучения в экономических экспериментах
ГЛАВЛЕНИЕ ДИССЕРТАЦИИ
кандидат наук Чернов Григорий Витальевич
Introduction

Problem description. Modern economics increasingly becomes an experimental science. Laboratory and field experiments, natural and quasi-experiments are everywhere in the economics literature. Experimental results have shed light on a wide range of questions, ranging from individual rationality, heuristics and biases, to efficiency and implementation of public policy programs. One of the main goals of experimental research is to improve our understanding of the mechanisms and reasons for making particular economic decisions. Yet progress in this direction so far has been limited. On the one hand, human decisions result from complicated, multi-dimensional processes, most of which remain unobservable or unidentifiable to the observer. On the other, human decision-makers are known to be boundedly rational and have limited abilities to figure out their best strategies even in relatively simple strategic environments, let alone to decode strategic intentions of the other player(s). In the heart of all these problems lies the problem of gradual accumulation of knowledge in the process of interactions, i.e. strategic learning.

The study of strategic learning is the main topic of this thesis. The work is threefold: we describe modeling approaches common in this theory, motivate the problem of experimental comparisons between the theories and prepare the ground to introduce new models with an example of such experimental comparison.

As we will learn from the first chapter, there is a huge pool of prospective models that fall into several large groups. These groups possess particular features: some models care about realized wins, some look only at the behavior of the opponent while ignoring that the opponent may do the same. There is also a hybridization between these models, resulting in "a model zoo". The problem then is to decide whether to fit a particular model or particular flavor of general model type to our data. Which models are more useful or more theoretically sound than others, which are observationally equivalent and can be safely substituted by simpler models - these are open scientific questions. We propose to start answering those questions by finding testable assumptions between classes of models and their specifications. We show that it can be done by simulations because the resulting empirical distributions reflect both finite-sample and asymptotic properties of experimental samples.

We then discuss the common empirical problem for model comparisons - we have no analytical and general way to compare models and to establish whether one model explains behavior better than another or even that it is better in clearly defined conditions (specific experimental game). Moreover, our econometric tools of comparison are lacking in this context - even when we can say that one model fits the data "better", it is hard to specify "by how much" and "how robust" is this comparison even for a different realization of the same game. The second chapter covers Formal Theory Approach (FTA) to the experimental problem: how can we identify and estimate the model within the laboratory